A GRU-Based Method for Predicting Intention of Aerial Targets

<table class="table-group" id="tab1"><tr><td><table class="table"><tr><td class="thead-hr" colspan="3"><hr/></td></tr><tr class="thead"><td class="align_left">Method</td><td class="align_center">Error (10<sup>−5</sup>)</td><td class="align_center">Prediction time (ms)</td></tr><tr><td class="thead-hr" colspan="3"><hr/></td></tr><tr><td class="align_left">RNN</td><td class="align_center">8.14</td><td class="align_center">0.089</td></tr><tr><td class="align_left">LSTM</td><td class="align_center">2.75</td><td class="align_center">0.133</td></tr><tr><td class="align_left">GRU</td><td class="align_center">2.46</td><td class="align_center">0.110</td></tr><tr><td class="align_left">BiLSTM</td><td class="align_center">1.29</td><td class="align_center">0.311</td></tr><tr><td class="align_left">BiGRU</td><td class="align_center">1.16</td><td class="align_center">0.202</td></tr><tr class="table-tr"><td colspan="3"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Experimental results of five models.</div>

Computational Intelligence and Neuroscience

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Table 1

Table 1: A GRU-Based Method for Predicting Intention of Aerial Targets 